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Multi-agent deep Q-network for traffic signal control under rainfall: a case study on Sunway city, Malaysia

In most urban areas, traffic congestion is a vexing, complex and growing issue day by day. Reinforcement learning (RL) enables a single decision maker (or an agent) to learn and make optimal actions in an independent manner, while multi-agent reinforcement learning (MARL) enables multiple agents to...

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Main Author: Faizan, Rasheed
Format: Thesis
Published: 2021
Subjects:
HE Transportation and Communications
Q Science (General)
Online Access:http://eprints.sunway.edu.my/2400/
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spelling my.sunway.eprints.24002023-09-27T08:47:32Z http://eprints.sunway.edu.my/2400/ Multi-agent deep Q-network for traffic signal control under rainfall: a case study on Sunway city, Malaysia Faizan, Rasheed HE Transportation and Communications Q Science (General) In most urban areas, traffic congestion is a vexing, complex and growing issue day by day. Reinforcement learning (RL) enables a single decision maker (or an agent) to learn and make optimal actions in an independent manner, while multi-agent reinforcement learning (MARL) enables multiple agents to exchange knowledge, learn, and make optimal joint actions in a collaborative manner. The integration of the newly emerging deep learning and the traditional RL approach has created an advanced technique called deep Q-network (DQN) that has shown promising results in solving high-dimensional and complex problems, including traffic congestion. In this research, DQN is embedded in traffic signal control to solve traffic congestion issue, which has been plagued with the curse of dimensionality, whereby the representation of the operating environment can be highly dimensional and complex when the traditional RL approach is used. Most importantly, this research proposes multi-agent DQN (MADQN) and investigates its use to further address the curse of dimensionality under traffic network scenarios with high traffic volume and disturbances. To investigate the effectiveness of our proposed scheme, a case study based on an urban area, namely Sunway City in Malaysia, is conducted. We evaluate our scheme via simulation using a traffic network simulator called simulation of urban mobility (SUMO) and a simulation tool called MATLAB. Simulation results show that our proposed scheme increases the throughput by [59, 97] and [54, 96] vehicles for recurring and non-recurring traffic congestions, respectively, as well as reduces the queue length by [2, 9] and [2, 10] vehicles for recurring and non-recurring traffic congestions, respectively, and the waiting time by [0, 9] seconds for both types of traffic congestions. 2021 Thesis NonPeerReviewed Faizan, Rasheed (2021) Multi-agent deep Q-network for traffic signal control under rainfall: a case study on Sunway city, Malaysia. Masters thesis, Sunway University.
institution Sunway University
building Sunway Campus Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Sunway University
content_source Sunway Institutional Repository
url_provider http://eprints.sunway.edu.my/
topic HE Transportation and Communications
Q Science (General)
spellingShingle HE Transportation and Communications
Q Science (General)
Faizan, Rasheed
Multi-agent deep Q-network for traffic signal control under rainfall: a case study on Sunway city, Malaysia
description In most urban areas, traffic congestion is a vexing, complex and growing issue day by day. Reinforcement learning (RL) enables a single decision maker (or an agent) to learn and make optimal actions in an independent manner, while multi-agent reinforcement learning (MARL) enables multiple agents to exchange knowledge, learn, and make optimal joint actions in a collaborative manner. The integration of the newly emerging deep learning and the traditional RL approach has created an advanced technique called deep Q-network (DQN) that has shown promising results in solving high-dimensional and complex problems, including traffic congestion. In this research, DQN is embedded in traffic signal control to solve traffic congestion issue, which has been plagued with the curse of dimensionality, whereby the representation of the operating environment can be highly dimensional and complex when the traditional RL approach is used. Most importantly, this research proposes multi-agent DQN (MADQN) and investigates its use to further address the curse of dimensionality under traffic network scenarios with high traffic volume and disturbances. To investigate the effectiveness of our proposed scheme, a case study based on an urban area, namely Sunway City in Malaysia, is conducted. We evaluate our scheme via simulation using a traffic network simulator called simulation of urban mobility (SUMO) and a simulation tool called MATLAB. Simulation results show that our proposed scheme increases the throughput by [59, 97] and [54, 96] vehicles for recurring and non-recurring traffic congestions, respectively, as well as reduces the queue length by [2, 9] and [2, 10] vehicles for recurring and non-recurring traffic congestions, respectively, and the waiting time by [0, 9] seconds for both types of traffic congestions.
format Thesis
author Faizan, Rasheed
author_facet Faizan, Rasheed
author_sort Faizan, Rasheed
title Multi-agent deep Q-network for traffic signal control under rainfall: a case study on Sunway city, Malaysia
title_short Multi-agent deep Q-network for traffic signal control under rainfall: a case study on Sunway city, Malaysia
title_full Multi-agent deep Q-network for traffic signal control under rainfall: a case study on Sunway city, Malaysia
title_fullStr Multi-agent deep Q-network for traffic signal control under rainfall: a case study on Sunway city, Malaysia
title_full_unstemmed Multi-agent deep Q-network for traffic signal control under rainfall: a case study on Sunway city, Malaysia
title_sort multi-agent deep q-network for traffic signal control under rainfall: a case study on sunway city, malaysia
publishDate 2021
url http://eprints.sunway.edu.my/2400/
_version_ 1779442531106291712
score 13.223943

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